{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 第四章  Numpy库基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['figure.facecolor']=(1,1,1,1)\n",
    "rcParams['font.size']=13\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据类型转变，直接astype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('S4')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_strings=np.array(['1.25','3.45','5.65'],dtype=np.string_)\n",
    "numeric_strings.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_num=numeric_strings.astype(float)\n",
    "new_num.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meshgrid函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "points=np.arange(-5,5,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xs,ys=np.meshgrid(points,points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z=np.sqrt(xs**2+ys**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GyuYCU5fJxMyifJYH5KCVgVmUjnSR3hMLKJun9dbD8tJa5y0bGczmeGBWV2Fg7SJwWekF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\nJiKnAfghAC8ezwXAQwC8bjR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XgdZrKyut8VlaeoL0jXo+DOCeWfs5HtgdAHwBwMtE5IMi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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e091cd320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(z,cmap=plt.cm.gray)\n",
    "plt.colorbar()\n",
    "plt.title('Image plot of $\\sqrt{x^2+y^2} for a grid of values$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制3D图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x26e09541908>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N6iPLzZgRfGQixTmB8+sR3F+OIvICeVpjkhb/uxT+P/B35Bx2IlL86le/yrVr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r/P48ePSafTfPazn71y0/hA6zhw7x8Y6v+zYfT7DjIPKu7hbUElLVJYQlGQCiaPJak7hs5DSfLY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\noSigpKhQKBQKRQElRYVCoVAoCvz/yPEknMhPuBEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e0b4c7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "ax=Axes3D(fig)\n",
    "ax.plot_surface(xs,ys,z,cmap=plt.cm.cool)\n",
    "ax.contourf(xs,ys,z,zdir='z',offset=-1,cmap=plt.cm.hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组的逻辑运算\n",
    "数组过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1, 3.1, 1.3, 1.4, 6.1]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarr=[1.1,1.2,1.3,1.4,1.5]\n",
    "yarr=[2.1,3.1,4.1,5.1,6.1]\n",
    "cond=[True,False,True,True,False]\n",
    "#列表推导式\n",
    "result=[x if c else y for x,c,y in zip(xarr,cond,yarr)]  \n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ZIP函数\n",
    "将多个列表按次数组合成一个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.1, True, 2.1)\n",
      "(1.2, False, 3.1)\n",
      "(1.3, True, 4.1)\n",
      "(1.4, True, 5.1)\n",
      "(1.5, False, 6.1)\n"
     ]
    }
   ],
   "source": [
    "zz=zip(xarr,cond,yarr)\n",
    "for i in zz:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.where()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.1,  3.1,  1.3,  1.4,  6.1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=np.where(cond,xarr,yarr)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.45423541,  0.5776959 ,  0.93541483, -0.63918771],\n",
       "       [ 0.07467217,  0.10725573, -0.89418469,  0.79132948],\n",
       "       [ 0.07010234, -0.61952415, -1.09334831, -2.43654636],\n",
       "       [-0.32574405,  0.83823878, -0.86460255, -0.07790466]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=np.random.randn(4,4)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.        ,  2.        ,  2.        , -0.63918771],\n",
       "       [ 2.        ,  2.        , -0.89418469,  2.        ],\n",
       "       [ 2.        , -0.61952415, -1.09334831, -2.43654636],\n",
       "       [-0.32574405,  2.        , -0.86460255, -0.07790466]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(arr>0,2,arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.where()嵌套"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 0, 3])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1=np.array([True,False,True,True,False])\n",
    "cond2=np.array([False,True,True,True,False])\n",
    "np.where(cond1 & cond2 ,0,\n",
    "         np.where(cond1,1,\n",
    "                  np.where(cond2,2,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数组逻辑运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**与**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False, True, True, True, False]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1=[True,False,True,True,False]\n",
    "l2=[False,True,True,True,False]\n",
    "l1 and l2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**异或**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1^cond2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bitwise_xor(cond1,cond2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**布尔型转化为0,1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 2, 3])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=1*(cond1^cond2)+2*(cond1 & cond2) +3*~(cond1|cond2)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**对 `True` 值进行计数**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr=np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(arr>0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bools=np.array([False,False,False,False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**bools.any( )**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**bools.all( )**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools=np.array([True,True,True,True])\n",
    "bools.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**所有非0元素被当做 `True` **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools=np.array([1,1,1,1,1])\n",
    "bools.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.17428235, -0.02747748,  0.3457126 ],\n",
       "       [ 0.56850584,  0.87412319,  0.81984339],\n",
       "       [-0.57500536, -0.19172052, -1.22326645],\n",
       "       [ 0.90221786,  1.51466539, -1.11019579],\n",
       "       [-0.61281105, -0.87598712,  0.27732713]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=np.random.randn(5,3)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认在1轴上排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02747748,  0.17428235,  0.3457126 ],\n",
       "       [ 0.56850584,  0.81984339,  0.87412319],\n",
       "       [-1.22326645, -0.57500536, -0.19172052],\n",
       "       [-1.11019579,  0.90221786,  1.51466539],\n",
       "       [-0.87598712, -0.61281105,  0.27732713]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sort()   \n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arr.sort( ) 改变原数组\n",
    "\n",
    "np.sort( ) 创建副本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02747748,  0.17428235,  0.3457126 ],\n",
       "       [ 0.56850584,  0.81984339,  0.87412319],\n",
       "       [-1.22326645, -0.57500536, -0.19172052],\n",
       "       [-1.11019579,  0.90221786,  1.51466539],\n",
       "       [-0.87598712, -0.61281105,  0.27732713]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 查找分位数\n",
    "返回第5%的数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.5308582250147977"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_arr=np.random.randn(1000)\n",
    "large_arr.sort() #改变原数组\n",
    "percent_5=large_arr[int(0.05*len(large_arr))]\n",
    "percent_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 集合运算\n",
    "方法|说明\n",
    ":---:|:---\n",
    "`unique(x)` | 范围唯一值，并进行排序，类似于set()\n",
    "`intersect1d(x,y)`|交集\n",
    "`union1d(x,y)`|并集\n",
    "`in1d`|存在性确认并过滤 \n",
    "`setdiff1d(x,y)`|in x not in y\n",
    "`setxor1d(x,y)`|异或，in x or y, nor in x and y at the same time\n",
    "存在性确认并过滤 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=np.array([0, 1, 2, 5, 0])\n",
    "states=[0,2]\n",
    "mask=np.in1d(test,states)\n",
    "test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask=np.in1d(test,states,invert=True)\n",
    "test[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从文件读取数据\n",
    "载入文件相对路径：data与本文件同级目录<br>\n",
    "错误：`/data/ch04/array_ex.txt`<br>\n",
    "正确：`data/ch04/array_ex.txt`<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.580052,  0.18673 ,  1.040717,  1.134411],\n",
       "       [ 0.194163, -0.636917, -0.938659,  0.124094],\n",
       "       [-0.12641 ,  0.268607, -0.695724,  0.047428],\n",
       "       [-1.484413,  0.004176, -0.744203,  0.005487],\n",
       "       [ 2.302869,  0.200131,  1.670238, -1.88109 ],\n",
       "       [-0.19323 ,  1.047233,  0.482803,  0.960334]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=np.loadtxt('data/ch04/array_ex.txt',delimiter=',')\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性代数\n",
    "**点乘**<br>\n",
    "即是一个 `方法` 也是一个 `函数`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  28.,   64.],\n",
       "       [  67.,  181.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.array([[1.,2.,3.],[4.,5.,6.]])\n",
    "y=np.array([[6.,23.],[-1,7],[8,9]])\n",
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  28.,   64.],\n",
       "       [  67.,  181.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性代数函数[numpy.linalg](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)\n",
    "\n",
    "常用线性代数函数\n",
    "![常用线性代数函数](https://github.com/ds17/file_saved/blob/master/%E5%B8%B8%E7%94%A8numpy.linalg%E5%87%BD%E6%95%B0.jpg?raw=true)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机数生成\n",
    "numpy.random\n",
    "![numpy.random](https://github.com/ds17/file_saved/blob/master/numpy.random%E5%87%BD%E6%95%B0.jpg?raw=true)\n",
    "*上传到 `GitHub` 的图片文件名中不可包含特殊字符，否则图片地址在jupyter中无法渲染*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机漫步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 纯Python实现\n",
    "同等概率的出现 `a,b`\n",
    "```python\n",
    "target= a if random.randint(0,1) else b  #randint(0,1)只能同概率的出现0/1， 非0值看作True\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "position=0\n",
    "walk=[position]\n",
    "steps=1000\n",
    "for i in range(1000):\n",
    "    step= 1 if random.randint(0,1) else -1\n",
    "    position+=step\n",
    "    walk.append(position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26e0b6bab38>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BTdqKyM+B84AIsBb4b2PMLnvftcClQCfwTWPMCwNsq1LA9FVLd/u8v7665zj1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\nfF6VdPKM+oSE9A6O22U+SDp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knjMPHMNXT5qc9SxUleVlsVDKInD9OQfE9v3giSVAcvrG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e091cd860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(walk)\n",
    "plt.title('Random walk with +1/-1 steps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26e0d8dd0f0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C/zMAY4loJBEVAzgPwMuZaszQslXh7XE54IwOiqPj9bgiJp0upig0hn3KWHxBEPKdjNrw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e0d89ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsteps=1000\n",
    "draws=np.random.randint(0,2,nsteps)\n",
    "walk_step=np.where(draws>0,1,-1)\n",
    "walks=walk_step.cumsum()\n",
    "plt.plot(walks)\n",
    "plt.title('Random walk with +1/-1 steps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`True` 为1，`False` 为0\n",
    "\n",
    "寻找第一个 True 值位置，即 array.argmax()\n",
    "\n",
    "寻找第一个 False 值位置，即 array.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.abs(walks)>10).argmax()   #找到第一个离中心10步的位置\n",
    "#argmax()会对整个array扫描"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26e0cfdaa20>]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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biRbJaTuktxudERE3t9yM6cN74dGFM3DhlEF6mywYCcZqT2bEZojsyn/pdTv1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\nhmGYEoEFPsMwTInAAp9hGKZE+H9l4Am1RBikxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e0ce17518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "walk_path=np.random.randint(0,2,size=(10,1000))\n",
    "walk_steps=np.where(walk_path>0,1,-1)\n",
    "walks_=walk_steps.cumsum(1)\n",
    "plt.plot(walks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何将在一个figure上绘制多个图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
